jpayne@1: # SeqSero2 alpha-test version
jpayne@1: Salmonella serotyping from genome sequencing data
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jpayne@1: 
jpayne@1: # Introduction 
jpayne@1: SeqSero2 is a pipeline for Salmonella serotype determination from raw sequencing reads or genome assemblies. This is a alpha test version. A web app will be available soon.
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jpayne@1: 
jpayne@1: # Dependencies 
jpayne@1: SeqSero has two modes:
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jpayne@1: (A) k-mer based mode (default), which applies unique k-mers of serotype determinant alleles to determine Salmonella serotypes in a fast speed. Special thanks to Dr. Hendrik Den Bakker for his significant contribution to this mode, details can be found in [SeqSeroK](https://github.com/hcdenbakker/SeqSeroK) and [SalmID](https://github.com/hcdenbakker/SalmID).
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jpayne@1: K-mer mode is a independant pipeline, it only requires:
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jpayne@1: 1. Python 3;
jpayne@1: 2. [SRA Toolkit](http://www.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?cmd=show&f=software&m=software&s=software) (optional, just used to fastq-dump sra files);
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jpayne@1: (B) allele based mode (if users want to extract serotype determinant alleles), which applies a hybrid approach of reads-mapping and micro-assembly.
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jpayne@1: Allele mode depends on:
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jpayne@1: 1. Python 3; 
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jpayne@1: 2. [Burrows-Wheeler Aligner](http://sourceforge.net/projects/bio-bwa/files/); 
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jpayne@1: 3. [Samtools](http://sourceforge.net/projects/samtools/files/samtools/);
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jpayne@1: 4. [NCBI BLAST](https://blast.ncbi.nlm.nih.gov/Blast.cgi?PAGE_TYPE=BlastDocs&DOC_TYPE=Download);
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jpayne@1: 5. [SRA Toolkit](http://www.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?cmd=show&f=software&m=software&s=software);
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jpayne@1: 6. [SPAdes](http://bioinf.spbau.ru/spades);
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jpayne@1: 7. [Bedtools](http://bedtools.readthedocs.io/en/latest/);
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jpayne@1: 8. [SalmID](https://github.com/hcdenbakker/SalmID).
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jpayne@1: 
jpayne@1: # Executing the code 
jpayne@1: Make sure all SeqSero2 and its dependency executables are added to your path (e.g. to ~/.bashrc). Then type SeqSero2_package.py to get detailed instructions.
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jpayne@1:     Usage: SeqSero2_package.py 
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jpayne@1:     -m <string> (which mode to apply, 'k'(kmer mode), 'a'(allele mode), default=k)
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jpayne@1:     -t <string> (input data type, '1' for interleaved paired-end reads, '2' for separated paired-end reads, '3' for single reads, '4' for genome assembly, '5' for nanopore fasta, '6'for nanopore fastq)
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jpayne@1:     -i <file> (/path/to/input/file)
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jpayne@1:     -p <int> (number of threads for allele mode, if p >4, only 4 threads will be used for assembly since the amount of extracted reads is small, default=1) 
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jpayne@1:     -b <string> (algorithms for bwa mapping for allele mode; 'mem' for mem, 'sam' for samse/sampe; default=mem; optional; for now we only optimized for default "mem" mode)
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jpayne@1:     -d <string> (output directory name, if not set, the output directory would be 'SeqSero_result_'+time stamp+one random number)
jpayne@1: 	
jpayne@1: 	-c <flag> (if '-c' was flagged, SeqSero2 will use clean mode and only output serotyping prediction without the directory containing log files)
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jpayne@1: 
jpayne@1: # Examples
jpayne@1: K-mer mode:
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jpayne@1:     # K-mer (default), for separated paired-end raw reads ("-t 2")
jpayne@1: 	SeqSero2_package.py -t 2 -i R1.fastq.gz R2.fastq.gz
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jpayne@1: 	# K-mer (default), for assemblies ("-t 4", assembly only predcited by K-mer mode)
jpayne@1: 	SeqSero2_package.py -t 4 -i assembly.fasta
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jpayne@1: Allele mode:
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jpayne@1:     # Allele mode ("-m a"), for separated paired-end raw reads ("-t 2"), use 10 threads in mapping and assembly ("-p 10")
jpayne@1: 	SeqSero2_package.py -m a -p 10 -t 2 -i R1.fastq.gz R2.fastq.gz
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jpayne@1: 	
jpayne@1: # Output 
jpayne@1: Upon executing the command, a directory named 'SeqSero_result_Time_your_run' will be created. Your result will be stored in 'Seqsero_result.txt' in that directory. And the assembled alleles can also be found in the directory if using "-m a" (allele mode).
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jpayne@1: 
jpayne@1: # Citation
jpayne@1: Zhang S, Yin Y, Jones MB, Zhang Z, Deatherage Kaiser BL, Dinsmore BA, Fitzgerald C, Fields PI, Deng X.  
jpayne@1: Salmonella serotype determination utilizing high-throughput genome sequencing data.  
jpayne@1: **J Clin Microbiol.** 2015 May;53(5):1685-92.[PMID:25762776](http://jcm.asm.org/content/early/2015/03/05/JCM.00323-15)